Datasets:

kbora
/

CS-CLIP-Training

license: cc-by-4.0
pretty_name: CS-CLIP Training Annotations
language:
  - en
tags:
  - image-text-retrieval
  - compositionality
  - clip
  - arxiv:2602.23906
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

CS-CLIP Training Annotations

Prepared training annotations for Half-Truths Break Similarity-Based Retrieval, NeurIPS 2026. Bora Kargi, Arnas Uselis, Seong Joon Oh.

This release contains 410,340 caption records from the eight annotation files used by the reported CS-CLIP run. All reference COCO train2014 images. Caption counts are not unique image counts. Images are downloaded separately from COCO.

The verified Half-Truth evaluation dataset is released separately.

Train using the original files

The archive preserves the training JSON files exactly, including their original field names. The CS-CLIP repository provides training code.

hf download kbora/CS-CLIP-Training original/training-json.tar.gz \
  --repo-type dataset --local-dir datasets/CS-CLIP-Training
mkdir -p datasets/CS-CLIP-Training/json
tar -xzf datasets/CS-CLIP-Training/original/training-json.tar.gz \
  -C datasets/CS-CLIP-Training/json

image_path is relative to the image root, for example datasets/COCO/train2014/COCO_train2014_000000057870.jpg. With this directory layout, set IMAGE_ROOT=..

Browse the annotations

from datasets import load_dataset
samples = load_dataset("kbora/CS-CLIP-Training", split="train")

The Parquet view exposes the main training fields in a tabular schema:

sample_id, original_caption, image_path: caption and image references.
entities: extracted positive units, called positive_components in the original JSON.
entity_foils: rows of positive, negative, and change_type, flattened from negative_components.
relations_json: JSON-encoded relation units and their matched foils, preserving nested source fields.
swap_negatives: full-caption shuffled negatives.

Use the original archive for exact training inputs; the Parquet files are a browsing view. manifest.json records each original file's SHA-256 and sample count.

Construction and limitations

The annotations contain automatically generated entity/relation units, matched foils, and shuffled caption negatives. They are not human-verified training labels. An image-grounded VLM audit of 1,000 training foils estimated a 24.6% false-negative rate: some nominally incorrect foils are true for the image. The paper's evaluation uses a separate human-verified suite. The training archive is distributed as used, including that noise.

Source assets and licensing

The authors’ generated annotations are released under CC BY 4.0. This grant covers their contributions, not third-party source captions or images.

Source captions and images come from COCO. COCO images are not included in this repository and retain their original rights and terms. An annotation license does not relicense source assets.

Citation

@inproceedings{kargi2026halftruths,
  title={Half-Truths Break Similarity-Based Retrieval},
  author={Kargi, Bora and Uselis, Arnas and Oh, Seong Joon},
  booktitle={Advances in Neural Information Processing Systems},
  year={2026},
  url={https://arxiv.org/abs/2602.23906}
}